Regenerative braking is one of the enablers of hybrid vehicle technologies. It has been found that 15%˜30% fuel economy improvements over a non-regenerative braking-capable vehicle can be achieved using regenerative braking. Trip information has been used to control HEV/PHEV/BEV and other types of hybrid vehicles in a most fuel-efficient fashion. Related to trip planning is a concept known as “energy to empty” for PHEV and BEVs, which approximately provides total energy available for propulsion purposes in a vehicle. This concept is closely-related to the “distance to empty” concept, which refers to the distance a vehicle can travel before all in-vehicle energy resources are exhausted to the lowest possible limits. Methods for “energy to empty” calculations generally include three parts: (1) energy from a thermal source (gas tank); (2) energy from an energy storage source (battery) based on initial state of charge of the battery; and (3) regenerative braking energy.
One of the most important applications related to “energy to empty” calculations is to find the most energy-efficient route for vehicle travel in trip planning. Several approaches to “energy to empty” calculations are known. These include a path-dependent approach for optimal energy management in HEV control in which considered inputs include static information such as road grade, segment average speed and traffic condition; on-board navigation system for EV and HEV management, in which ambient temperature information is used to determine end SOC value of HEV battery and other static information such as speed, driver behavior (via pattern recognition), grade information are considered inputs; and fuel-efficient driving area calculation and display, in which road types and grades such as uphill, downhill and “dangerous road” are considered inputs.
None of the conventional methods of calculating regenerative braking energy seems to have considered variations in regenerative braking energy recovery with fixed grade and traffic information along with dynamic information such as varying ambient temperatures and precipitation, road condition (roughness and surface mu information) together with related vehicle system controller regen-related calibration maps (i.e., how to discount regenerative braking in order to maintain traction control).
In reality, regenerative braking depends heavily on traction control needs and goes beyond grade or speed. Accordingly, calculation of regenerative braking energy based solely on static information such as grade, speed (as profiled), stop-sign and statistical traffic is insufficiently accurate.
Therefore, there is a need for a method which accurately calculates regenerative braking energy when a trip is planned in order to facilitate overall fuel economy improvement when dynamic information such as route ambient temperature information, precipitation information (forecast and past history) and/or road conditions (mu-identification) are available in advance.